Diagnostic Accuracy of a Novel Machine Learning Algorithm to Estimate Gestational Age

NCT ID: NCT05433519

Last Updated: 2024-05-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-07-27

Study Completion Date

2023-11-13

Brief Summary

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This is a prospective cohort study of women enrolled early in pregnancy, with randomization to determine the timing of three follow-up visits in the second and third trimester. At each of these follow-up visits, investigators will assess gestational age with the FAMLI technology and compare that estimate to the known gestational age established early in pregnancy.

Detailed Description

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The primary purpose of this research is to assess the diagnostic accuracy of the FAMLI Technology, a novel machine learning-based tool for gestational age assessment that can run on a smart phone or tablet. Study staff will enroll 400 pregnant volunteers prior to 14 completed gestational weeks and obtain accurate "ground truth" gestational age dating with standard ultrasound biometry, using the crown-rump length. These participants will then be asked to return for three follow-up visits, which will include a routine sonogram performed by a trained sonographer and the collection of a set of blind sweep cineloop videos using a low-cost, battery-operated device. The research will be conducted in Chapel Hill, North Carolina (at the University of North Carolina Hospital and/or sites associated with UNC OBGYN) and in Lusaka, Zambia (at the University Teaching Hospital or Kamwala District Health Centre). Approximately equal numbers of participants will be enrolled from each country.

Conditions

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Gestational Age Machine Learning Pregnancy Related

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Pregnant Women

Pregnant women with gestational age established at less than 14 weeks of gestation

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* 18 years of age or older
* viable intrauterine pregnancy at less than 14 0/7 weeks of gestation
* ability and willingness to provide written informed consent
* intent to remain in current geographical area of residence for the duration of study
* willingness to adhere to study procedures

Exclusion Criteria

* maternal body mass index = 40 kg/m\^2
* multiple gestation (i.e., twins or higher order)
* major fetal malformation or anomaly
* any other condition (social or medical) that, in the opinion of the study staff, would make study participation unsafe or complicate data interpretation.
Minimum Eligible Age

18 Years

Maximum Eligible Age

59 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No

Sponsors

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Bill and Melinda Gates Foundation

OTHER

Sponsor Role collaborator

University of North Carolina, Chapel Hill

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Jeff Stringer, MD

Role: PRINCIPAL_INVESTIGATOR

University of North Carolina

Locations

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University of North Carolina

Chapel Hill, North Carolina, United States

Site Status

University Teaching Hospital

Lusaka, , Zambia

Site Status

Countries

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United States Zambia

References

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Stringer JSA, Pokaprakarn T, Prieto JC, Vwalika B, Chari SV, Sindano N, Freeman BL, Sikapande B, Davis NM, Sebastiao YV, Mandona NM, Stringer EM, Benabdelkader C, Mungole M, Kapilya FM, Almnini N, Diaz AN, Fecteau BA, Kosorok MR, Cole SR, Kasaro MP. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps. JAMA. 2024 Aug 27;332(8):649-657. doi: 10.1001/jama.2024.10770.

Reference Type DERIVED
PMID: 39088200 (View on PubMed)

Provided Documents

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Document Type: Study Protocol

View Document

Document Type: Statistical Analysis Plan

View Document

Other Identifiers

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21-3115

Identifier Type: -

Identifier Source: org_study_id

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